Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2019
Authors
B. S. Steidinger T. W. Crowther J. Liang M. E. Van Nuland G. D. A. Werner P. B. Reich G. J. Nabuurs S. de-Miguel M. Zhou N. Picard B. Herault X. Zhao C. Zhang D. Routh Meinrad Abegg C. Yves Adou Yao Giorgio Alberti Angelica Almeyda Zambrano Esteban Alvarez-Davila Patricia Alvarez-Loayza Luciana F. Alves Christian Ammer Clara Antón Fernández Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo Aymard Timothy Baker Radomir Bałazy Olaf Bánki Jorcely Barroso Meredith Bastian Jean-Francois Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Frans Bongers Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Roel Brienen Eben Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo Cesar Goran Cesljar Robin Chazdon Han Y. H. Chen Douglas Sheil K. G. PeayAbstract
The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools1,2, sequester carbon3,4 and withstand the effects of climate change5,6. Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species7, constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species.
Authors
Jari Vauhkonen Ambros Berger Thomas Gschwantner Klemens Schadauer Philippe Lejeune Jérôme Perin Mikhail Pitchugin Radim Adolt Miroslav Zeman Vivian Kvist Johannsen Sebastian Kepfer-Rojas Allan Sims Claire Bastick François Morneau Antoine Colin Susann Bender Pál Kovácsevics György Solti László Kolozs Dóra Nagy Kinga Nagy Mark Twomey John Redmond Patrizia Gasparini M. Notarangelo Maria Rizzo Kristaps Makovskis Andis Lazdins Ainars Lupikis Gintaras Kulbokas Clara Antón Fernández Francisco Castro Rego Leónia Nunes Gheorghe Marin Catalin Calota Damjan Pantić Dragan Borota Joerg Roessiger Michal Bosela Vladimír Šebeň Mitja Skudnik Patricia Adame Iciar Alberdi Isabel Cañellas Torgny Lind Renats Trubins Esther Thürig Golo Stadelmann Ben Ditchburn David Ross Justin Gilbert Lesley Halsall Markus Lier Tuula PackalenAbstract
No abstract has been registered
Authors
Jari Vauhkonen Ambros Berger Thomas Gschwantner Klemens Schadauer Philippe Lejeune Jérôme Perin Mikhail Pitchugin Radim Adolt Miroslav Zeman Vivian Kvist Johannsen Sebastian Kepfer-Rojas Allan Sims Claire Bastick François Morneau Antoine Colin Susann Bender Pál Kovácsevics György Solti László Kolozs Dóra Nagy Kinga Nagy Mark Twomey John Redmond Patrizia Gasparini Monica Notarangelo Maria Rizzo Kristaps Makovskis Andis Lazdins Ainars Lupikis Gintaras Kulbokas Clara Antón Fernández Francisco Castro Rego Leónia Nunes Gheorghe Marin Catalin Calota Damjan Pantić Dragan Borota Joerg Roessiger Michal Bosela Vladimír Šebeň Mitja Skudnik Patricia Adame Iciar Alberdi Isabel Cañellas Torgny Lind Renats Trubins Esther Thürig Golo Stadelmann Ben Ditchburn David Ross Justin Gilbert Lesley Halsall Markus Lier Tuula PackalenAbstract
• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh . The associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9 .
Authors
Clara Antón FernándezAbstract
No abstract has been registered
Authors
Kaiguang Zhao Michael A. Wulder Tongxi Hu Ryan Bright Qiusheng Wu Haiming Qin Yang Li Elizabeth Toman Bani Mallick Xuesong Zhang Molly BrownAbstract
Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single “best” model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics.
Abstract
Due to the potential for land-use–land-cover change (LULCC) to alter surface albedo, there is need within the LULCC science community for simple and transparent tools for predicting radiative forcings (ΔF) from surface albedo changes (Δαs). To that end, the radiative kernel technique – developed by the climate modeling community to diagnose internal feedbacks within general circulation models (GCMs) – has been adopted by the LULCC science community as a tool to perform offline ΔF calculations for Δαs. However, the codes and data behind the GCM kernels are not readily transparent, and the climatologies of the atmospheric state variables used to derive them vary widely both in time period and duration. Observation-based kernels offer an attractive alternative to GCM-based kernels and could be updated annually at relatively low costs. Here, we present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth's Radiant Energy System (CERES) Energy Balance and Filled (EBAF) products. When constructed on a 16-year climatology (2001–2016), we find that the CERES-based albedo change kernel – or CACK – agrees remarkably well with the mean kernel of four GCMs (rRMSE = 14 %). When the novel parameterization underlying CACK is applied to emulate two of the GCM kernels using their own boundary fluxes as input, we find even greater agreement (mean rRMSE = 7.4 %), suggesting that this simple and transparent parameterization represents a credible candidate for a satellite-based alternative to GCM kernels. We document and compute the various sources of uncertainty underlying CACK and include them as part of a more extensive dataset (CACK v1.0) while providing examples showcasing its application.
Abstract
Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.
Abstract
Deforestation influences surface properties such as surface roughness, resulting in changes in the surface energy balance and surface temperature. Recent studies suggest that the biogeophysical effects are dominated by changing roughness, and it remains unclear whether this can be reconciled with earlier modeling studies that highlighted the importance of a reduction of evapotranspiration in the low latitudes and a reduction of net shortwave radiation at the surface in the high latitudes. To clarify this situation, we analyze the local effects of deforestation on surface energy balance and temperature in the MPI‐ESM climate model by performing three separate experiments: switching from forest to grass all surface properties, only surface albedo, and only surface roughness. We find that the locally induced changes in surface temperature are dominated by changes in surface roughness for the annual mean, the response of the diurnal amplitude, and the seasonal response to deforestation. For these three quantities, the results of the MPI‐ESM lie within the range of observation‐based data sets. Deforestation‐induced decreases in surface roughness contribute substantially to winter cooling in the boreal regions and to decreases in evapotranspiration in the tropics. By comparing the energy balance decompositions from the three experiments, the view that roughness changes dominate the biogeophysical consequences of deforestation can be reconciled with the earlier studies highlighting the relevance of evapotranspiration.
Abstract
No abstract has been registered
Authors
Kjersti Holt HanssenAbstract
No abstract has been registered